ProFIT: Prolog with Features, Inheritance and Templates

ProFIT: Prolog with Features, Inheritance and Templates Gregor Erbach Universit~t des Saarl~ndes Computerlinguistik D-66041 Saarbrficken, Germany e-mail: erbach~col i. uni-sb, de URL : http ://coli. uni-sb, de/~ erbach/ Abstract ProFIT is an extension of Standard Pro- log with Features, Inheritance and Tem- plates. ProFIT Mlows the programmer or grammar developer to declare an in- heritance hierarchy, features and tem- plates. Sorted feature terms can be used in ProFIT programs together with Pro- log terms to provide a clearer descrip- tion language for linguistic structures. ProFIT compiles all sorted feature terms into a Prolog term representation, so that the built-in Prolog term unification can be used for the unification of sorted feature structures, and no special uni- fication algorithm is needed. ProFIT programs are compiled into Prolog pro- grams, so that no meta-interpreter is needed for their execution. ProFIT thus provides a direct step from grammars de- veloped with sorted feature terms to Pro- log programs usable for practical NLP systems. 1 Introduction There are two key ingredients for building an NLP system: • a linguistic description • a processing model (parser, generator etc.) In the past decade, there have been diverging trends in the area of linguistic descriptions and in the area of processing models. Most large-scale linguistic descriptions make use of sorted feature formalisms, 1 but implementations of these for- malisms are in general too slow for building prac- tically usable NLP systems. Most of the progress in constructing efficient parsers and generators has been based on logic grammars that make use 1Sorted feature structures are sometimes referred to as typed feature structures, e.g. in Carpenter's "Logic of Typed Feature Structures." We follow the usage in Logic Programming and the recent HPSG literature. of ordinary prblog terms. We provide a general tool that brings together these developments by compiling sorted feature terms into a Prolog term representation, so that techniques from logic pro- gramming and logic grammars can be used to pro- vide efficient processing models for sorted feature grammars. In this introductory section, we discuss the ad- vantages of sorted feature formalisms, and of the logic grammar paradigm, and show how the two developments can be combined. The following sections describe the ProFIT language which pro- vides sorted feature terms for Prolog, and its im- plementation. 1.1 Grammar Development in Sorted Feature Formalisms Sorted feature formalisms are often used for the development of large-coverage grammars, because they are very well suited for a structured descrip- tion of complex linguistic data. Sorted feature terms have several advantages over Prolog terms as a representation langauge. 1. They provide a compact notation. Features that are not instantiated can be omitted; there is no need for anonymous variables. 2. Features names are mnemonic, argument po- sitions are not. 3. Adding a new feature to a sort requires one change in a declaration, whereas adding an argument to a Prolog functor requires changes (mostly insertion of anonymous vari- ables) to every occurence of the functor. 4. Specification of the subsort relationship is more …

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